6/16/2021

What is human mobility?

The geographic displacement of human beings in space and time, seen as individuals or groups. Barbosa et al., 2018

missing

Fig. 1 Individual movements.

missing

Fig. 2 Population trips.

How is human mobility supported by transport systems?

Through a variety of transport modes, e.g.,



ride-sourcing public transit private car

Transport modal disparities

  • Carbon intensity

  • Spatiotemporal distributions of travel time and trips

Background

  • Transportation presents a major challenge to curbing climate change.

  • Better informed policymaking requires up-to-date empirical data with good quality, low cost, and easy access.

  • Emerging data sources enable deep and new insights from large-scale collection of human movement and transport systems.

Fig. 3 Tweets and road networks (car + public transit) in Stockholm region

Research questions and present work

  1. What are the potentials and limitations of using emerging data sources for modelling mobility?

  2. How can new data sources be properly modelled for characterising transport modal disparities?

RQ # Scope Paper title
1 I Population heterogeneity From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data
II Travel demand Feasibility of estimating travel demand using geolocations of social media data
III A mobility model for synthetic travel demand from sparse individual traces
2 IV Travel time Disparities in travel times between car and transit: spatiotemporal patterns in cities
V Modal competition Ride-sourcing compared to its public-transit alternative using big trip data

Methodology

Fig. 4 Methodology



Method
Paper
I II III IV V
Data mining ✔️ ✔️
Mobility metrics and models ✔️ ✔️ ✔️ ✔️ ✔️
Methods in transport geography ✔️ ✔️

RQ1 Potentials and limitations of geotagged tweets

Geotagged tweets


The tweets with precise location information (GPS coordinates) when Twitter users actively choose to tag it.

  • Why Twitter?

    • Easy access, low cost, large spatial and population coverage.

  • Limitations of geotagged tweets

    • Biased population: young, highly-educated, urban residents.

    • Sparse sampling of the actual mobility.

    • Behaviour bias of reporting geolocations.

Limitations: sparse sampling of the actual mobility

  • Twitter users DO NOT geotweet every day.

  • Twitter users DO NOT geotweet every location visited.

Fig. 5 Sparsity issue.Data used in Paper III

Limitations: behaviour bias of overly reporting leisure/night activities

Uncommon places and leisure activities >> regularly visited places, e.g., home and workplace.

Fig. 6 Behaviour bias.Data used in Paper I

Limitations: not for commuting travel demand estimation

The reliability of estimated commuting trips using geotagged tweets is low.Paper II

Fig. 7A Commuting matrices.

Fig. 7B Commuting trip distance distributions.

Potentials at individual level: population heterogeneity on mobility

Fig. 8 Four types of travellers.Paper I

  • Local vs. Global traveller visits

    • Local: nearby locations.

    • Global: more distant locations.

  • Returner vs. Explorer explores around

    • Returner: one centralised location.

    • Explorer: decentralised locations that are distant from each other.

Potentials at population level: travel demand modelling

spatial scale sampling method sample size

  • Twitter data are more suitable for city level than national level.

  • The main obstacle of using Twitter data at a large spatial scale is the sparsity.

Fig. 9 National level (left) vs. city level (right).Paper II

Potentials at population level: travel demand modelling

spatial scale sampling method sample size

  • User-based data collection works better than area-based data collection:

    A much larger number of geotagged tweets, a more complete picture of travel demand.

  • A density-based approach is proposed to increase sample size:

    Fig. 10 Trip-based approach (left)Lee et al., 2019 vs. density-based approach (right).Paper II

Extending the use by innovative approaches

A density-based approach A mobility model

  • An individual-based mobility modelPaper III fills the gaps in sparse mobility data, particularly geolocations of social media data.

  • The model is designed to correct behaviour bias and sparsity issue.

Input: sparse mobility traces
that can not be directly converted to trips.

Output: synthesised mobility
converted to daily trips.

Extending the use by innovative approaches

A density-based approach A mobility model

  • The model-synthesised results have good agreements with the other data sources.

  • Characterising trip distance distributions (domestic) of global regions:

Fig. 11 Distributions of synthesised domestic trips.Paper III

RQ2 Transport modal disparities

Movements in context, data beyond geotagged tweets

Spatiotemporal patterns of travel time

Data fusion framework for travel time calculationPaper IV:

Distributions of geotagged tweets represents the dynamic attractiveness of locations in cities.

Fig. 12 Geotagged tweets as destinations by hour of day (Stockholm region).

Spatiotemporal patterns of travel time

Spatiotemporal dynamics of travel time ratio (R)Paper IV:

Fig. 13 Travel time ratio by hour of day (Sydney).

  • Travel time by PT is around twice as high as by car.

  • PT can compete with car use during peak rush hours in Stockholm and Amsterdam.

Fig. 14 Travel time ratio over 24 hours.

Modal competition: ride-sourcing vs. public transit

Data fusion approaches

For travel time calculation For open trip data analysis (ride-sourcing)

  • Collected from a large area and population but at a cost of rich detail.

  • Raw data: trip ID, pick-up and drop-off locations, pick-up and drop-off times, and cost.

Fig. 15 Data enrichment for ride-sourcing trip data.Paper V

Modal competition: ride-sourcing vs. public transit

Modal competition: ride-sourcing vs. public transit

Modal competition: ride-sourcing vs. public transit

A summary of answers

RQ1 Potentials and limitations for modelling mobility

  • Easy access, low cost, but with biased population, behaviour bias, and sparsity issue.

  • At the individual level, fundamental patterns are preserved.

  • At the population level:

    • a reasonably good source for the overall travel demand estimation but not commuting demand.

    • careful consideration on spatial scale, sampling method, and sample size.

  • Innovative approaches for correcting the biases and increasing the available data.

A summary of answers

RQ2 Characterising transport modal disparities

  • Importance of data fusion approaches, especially given more and more open but incomplete data.

  • Geotagged tweets is a good source for time-varying attractiveness of urban locations.

  • Public transit is virtually always slower than car and ride-sourcing.

  • For making public transit more competitive, spatiotemporal details add nuanced insights to identify gaps and opportunities.

Outlook

  • Extending the use of social media data for mobility modelling.

    • De-biasing the data source.

    • Long-distance travels.

    • Combining the textual information with the location part.

  • Generating global synthetic mobility data for improving travel demand projections.

    • Bigger picture: energy systems’ modelling for the transport sector.

Outlook

  • Combining multi-modal trip data for reducing transport carbon emissions.

    • Occupancy, shareability, and electrification of new mobility services provided by transportation network companies (TNC).

  • Introducing the perspective of networks into urban mobility research.

    • The relationship between user/traveller friendship networks (abstract) and their mobility networks (spatial).

    • How social segregation and spatial interactions shape each other?

Thanks for listening!